Bootstraping financial time series

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Show simple item record Ruiz Ortega, Esther Pascual, Lorenzo 2009-07-14T08:44:53Z 2009-07-14T08:44:53Z 2002-07
dc.identifier.bibliographicCitation Journal of Economic Surveys, 2002, vol. 16, n. 3, p. 271-300
dc.identifier.issn 1467-6419
dc.description.abstract It is well known that time series of returns are characterized by volatility clustering and excess kurtosis. Therefore, when modelling the dynamic behavior of returns, inference and prediction methods, based on independent and/or Gaussian observations may be inadequate. As bootstrap methods are not, in general, based on any particular assumption on the distribution of the data, they are well suited for the analysis of returns. This paper reviews the application of bootstrap procedures for inference and prediction of financial time series. In relation to inference, bootstrap techniques have been applied to obtain the sample distribution of statistics for testing, for example, autoregressive dynamics in the conditional mean and variance, unit roots in the mean, fractional integration in volatility and the predictive ability of technical trading rules. On the other hand, bootstrap procedures have been used to estimate the distribution of returns which is of interest, for example, for Value at Risk (VaR) models or for prediction purposes. Although the application of bootstrap techniques to the empirical analysis of financial time series is very broad, there are few analytical results on the statistical properties of these techniques when applied to heteroscedastic time series. Furthermore, there are quite a few papers where the bootstrap procedures used are not adequate.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Wiley-Blackwell
dc.relation.hasversion Contributions to Financial Econometrics: Theoretical and Practical Issues, 2002, . 35-64, ISBN: 140510743X, ISBN13: 9781405107433
dc.rights ©Wiley-Blackwell
dc.subject.other Forecasting
dc.subject.other GARCH models
dc.subject.other Non–Gaussian distributions
dc.subject.other Stochastic Volatility
dc.subject.other Variance ratio test
dc.subject.other Value–at–Risk (VaR)
dc.subject.other Technical Trading Rules
dc.subject.other Prediction
dc.title Bootstraping financial time series
dc.type article PeerReviewed
dc.description.status Publicado
dc.subject.eciencia Estadística
dc.identifier.doi 10.1111/1467-6419.00170
dc.rights.accessRights openAccess
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